Category: Sabermetrics

2016 MORPS projections are finally ready. Unlike the baseline projections published several weeks ago, these projections include all players projected to win a MLB roster spot on opening day. I have also included a number of impact rookies who are projected to join a roster early or mid-season in 2016. Rookie projections use stats generated during either minor league or international play. Factors are applied to adjust the stats to MLB equivalent stats. MORPS projections also account for expected adjustments in personal playing time.

The excel version of the projections include a key tab that defines all headings used in the projections. In short, fantasy baseball players that play in rotisserie leagues should key on the R-ROTO and ROTO columns. ROTO is a point value derived from weights on the categories in a standard 5×5 rotisserie league. R-ROTO is the player ranking based upon the ROTO point values. If you league uses a customer scoring system, you can use the projections in the categories of interest to customize your rankings. Fantasy baseball players that play in a more realistic format like Baseball Manager or a similar simulation league should reorder the pitching based upon OERV and the batting based upon RC. OERV stands for Out Earned Run Value. This stat attempts to value a pitcher by combining ERA with the value of number of innings pitched. This is a way for fantasy managers in simulation leagues to compare the value of a relief pitcher with a starter or a starter who pitches 200 innings with one that pitches 100 with a slightly lower ERA. RC is Runs Created. A league like Baseball Manager uses RC as a basis for the points they generate in their daily games. The more realistic the simulation, the closer the hitting will align with RC.

For those who like to resort the projections for their own fantasy system, make sure you filter out the players with a roster status of “N”. These players will most likely not make an opening day 25 man roster. Those players who were still in competition for a position were included with a roster status of “Y” in most cases. I posted the “N” players for those managers who have keeper leagues or deeper rosters that may want to pull one of these folks onto their list.

The 2016 MORPS baseline projections are ready. This is the third year we have provided baselines. These projections use all the models we have put together over the years for projecting player performance. This means that the projections are still based on four years of data, positional mean regression, etc. However, they do not account for a player changing positions, reductions in playing time, new players to the big leagues, etc. We entered all MLB player transactions into system since the end of the regular season last year. While this doesn’t guarantee that we have caught every trade, free agent move or player being waived; we are hoping that the majority of these type of transactions were captured in the system.

Some may like the baseline projections more than the final version. I read one review of MORPS in 2014 that criticized the fact that we took the time to model anticipate plate appearances and batters faced for each team before releasing our final projections. They didn’t consider that process “scientific”. Our perspective is that the modeling allows us to adjust the ratios between each stat and plate appearance or batter faced to account for situations that weren’t present the year before. This could be a player being part of a platoon when they played the position full-time the year before. It could be a reduction in playing time due to the appearance of a blockbuster free agent or anticipated rookie hitting the big leagues. It could also be a pitcher coming back from Tommy John surgery after being out of the game for over a year. Regardless of the situation, we believe that the modeling of plate appearances and batters faced for each team adds significant value to MORPS projections. This view is supported by our #1 ranking in 2014 for player projections using root mean square error (RMSE). If you still doubt our ability to accurately model these situations or you have an early fantasy draft and need something now, you’re in luck. You can use our baseline projections.

So…. without further ado, we present the 2016 MORPS Baseline Projections. The Batting and Pitching projections are available in excel and PDF formats. Follow the links below to download your copy.

If you player Roto baseball, you will find the projections already sorted in Roto Rank order. If you play a more realistic version of fantasy baseball, like BBM, you will need to re-sort the XLS spreadsheet in RC order for batters and OERV order for pitchers. Play Ball!

Each year we take a moment to review our projections against the actual season results. First lets look at the team projections. We definitely missed on the Kansas City Royals and their World Series win. On the positive side, we did predict playoff runs for the Mets, Cardinals, Dodgers, Pirates and Blue Jays. Our only miss in the NL was the Cubs over the Nationals. The AL is another story. In addition to the Royals, we also missed on the Astros, Rangers, and Yankees. Predicting 50% of the playoff participants isn’t bad considering the number of roster changes that happen during the course of the season.

For individual projections, we were pleasantly surprised that MORPS was the number one overall projection system in terms of Root Mean Square Error (RMSE) according to The Baseball Projection Project. The 2014 results were published in March on Fangraphs – click here. Based upon improvements implemented for 2015, our hope is that those results are replicated when 2015 results are published later this year. For fantasy owners, this means that MORPS has the lowest error rate of all published projection systems on the market. A lower error rate means that you can rely on the order that players are ranked within the MORPS projection system.

Overall predictive capability was another rating category tackled by The Baseball Projection Project. MORPS didn’t do as well in this category. Upon analysis, this is due to the regression to the mean built into the MORPS engine. This doesn’t have a huge impact on established players. It does have an impact on players with three or less years of experience or players returning from lengthy injuries. We’ll be looking into this further to determine if there is a way to compete effectively in both categories effectively in the future.

Going into 2016, we are confident that our free projection system stacks up quite well with all of the systems out there. This includes those that cost quite a bit of money to access.

MORPS projections are late coming this year. I’ve delivered a set of baseline projections several weeks ago. However, you’ll find that the actual projections have some drastic differences. I’m always amazed by the amount of player movement during the off season.

The Major-League Obie Role-Based Projection System (MORPS) uses four years of player performance data for all hitters. Since I started playing with Sabermetrics using Tango’s Marcel system, the first iteration of MORPS four years ago used the same formulas. After learning the basics, the batter formulas were adjusted to include the most recent four years of performance data. Adjustments were also made for player age, home ballpark data and expected playing time. The most complicated part of the system is the regression formulas. Tango provided formulas for his three year model. I had to crack open the math books to figure out how to transition the formulas to a four year model.

One of the most time consuming tasks in developing the system was determining the proper mean for player regression. If the goal was to ensure that the mean of all the projections competed favorably with end of year player means, the task would have been straight forward. However, my goal was to make the actual player projections as accurate as possible. “Role-Based” means that the player projections are regressed to position specific means. National League means are also separated from American League means.

While conducting research, I noticed that most projection systems used minor league stats as well as any available major league stats to project the future performance of young players. There are even formulas that anticipate player regression when entering the majors. The interesting thing is that Tango’s Marcel system does just as good at predicting rookie performance as other projection systems and he doesn’t use any minor league stats. Some players are great in the minors and simply can’t make the jump to the major leagues. Some players start out great, but find that major league pitchers start exploiting weaknesses they never knew they had. Others outperform all expectations. By calculating the reliability of a player’s projection using only major league data, MORPS adds a proportional dosage of a player’s positional mean to complete a rookie’s player projection. Since we are focused on individual player performance, I didn’t see the point of including all minor league stats when the results don’t seem to provide significant value. The last year of a rookies minor league or international season is included, with appropriate adjustments for competition, if no major league experience exists. While efforts have been made to adjust projections to reflect anticipated playing time, players who have a roster flag of “N” are projected using baseline projections only.

Pitching Projections

The formulas used to create pitcher projections are very similar to those that we have already discussed with batters. MORPS uses four years of data to create a pitcher projection. Adjustments are made for age, home field and anticipated role. The reliability of a projection is calculated based upon the amount of data available for a particular player. Someone with low reliability will regress more to a position specific mean than someone that has faced a lot of major league batters over the last four years.

The big difference between projecting pitchers and batters is the usage disparity between relief pitchers and starting pitchers. A good relief pitcher may face 350 batters in a season. A top end starting pitcher may pitch to 900 batters in a season. The plate appearances for position players are typically not dependent on role. A first baseman and shortstop may both have 600 plate appearances over the course of a year. Their position means will be different. First basement will typically have higher power stats while shortstops have higher speed stats. But, they are similar enough that their projections can be calculated using the same basic formulas. The disparity between relief and starting pitchers forces them to be calculated very differently. For months I struggled with pitching projections. When I finally figured out that starting pitchers and relief pitchers had to be calculated separately, everything fell in place.

You will notice a new stat category has been introduced that is unique to MORPS – OERV. OERV stands for out earned run value. This new stat attempts to rank pitchers based upon a combination of performance (earned runs allowed) and the number of outs generated for their team. For example, Aroldis Chapman is expected to have a slightly better ERA than Matt Cain. However, Cain is projected to pitch 211 innings compared to Chapman’s 175. As a result, Cain’s OERV is better than Chapman. For those that play rotisserie baseball, a combination stat like this may not have value. You just want to get the best players in each of X specific categories. Head to head simulation leagues, like baseball manager (BBM), use sabermetric calculations to determine daily winners. These leagues will probably find this new stat very useful. This stat attempts to answer the old question that every fantasy manager in these leagues ask on draft day – “When should I opt for a pitcher that eats innings over the pitcher with a lower ERA”.

Every year you see tons of websites predicting which MLB teams have made the right moves to get their team to the playoffs. Some make their predictions based upon their inane baseball IQ. Others use a popularity approach, which teams are getting the most press or the teams that have landed the big name free agents. Perhaps some sites use dart boards or drawing names from a hat. How else can you explain sites that predict the Cubs or Astros getting to the playoffs! Well, we are going to take a little different approach.

Bill James, the pioneer of baseball sabermetrics, created a formula to predict a team’s winning percentage called “The Pythagorean Expectation”. Without boring everyone with the fine details, this formula models the winning percentage of a team based upon runs scored and runs allowed. With anticipated starting and reserve lineups, MORPS has already projected runs created and runs allowed for each team in order to create individual projections. By feeding this data into a refined version of Bill James’ formula created by David Smyth, MORPS team win/loss records can be projected. The records are then adjusted slightly to show the number of games played within each division, league and inter-league matchups. This adds an element of anticipated strength of schedule to a set of formulas created to model the past rather than predict the future.

In the next few days/weeks, I will plan on releasing projected wins and losses for the teams in MLB. I was hoping that all the big name free agents would be off the board at this point, but progress can’t wait on hard-headed agents or budget conscious General Managers. Since my main goal leading up to spring training is to continue updating MORPS for upcoming fantasy drafts, I will begin releasing team projections very soon. When final free agents sign contracts, projected wins and losses may adjust slightly. I may go back and update team projections prior to the start of the season if time permits.

For those that are interested in the detail, I have outlined several the formulas below that are used in team win/loss projections.

The Pythagorean Expectation (developed by Bill James)

Pythagenpat formula (developed by David Smyth)

Exponent = ((r + ra)/g)0.287

Runs Created (developed by Bill James) – calculated for each individual player

The pitching projections took a little more time to get ready. I’ve added some logic this year that adjusts the historical numbers when a player moves from a relief to a starting role or vice versa. A good relief pitcher and good starting pitcher can have 500 to 600 difference in batters faced over the course of a season. This makes it impossible to directly translate a relief pitchers performance to a starting role or a starter into a relief role. I’m hoping that my new logic solved this puzzle, at least from the point of view of sabermetric projections.